Genetic interaction mapping with microfluidic-based single cell sequencing.
ABSTRACT: Genetic interaction mapping is useful for understanding the molecular basis of cellular decision making, but elucidating interactions genome-wide is challenging due to the massive number of gene combinations that must be tested. Here, we demonstrate a simple approach to thoroughly map genetic interactions in bacteria using microfluidic-based single cell sequencing. Using single cell PCR in droplets, we link distinct genetic information into single DNA sequences that can be decoded by next generation sequencing. Our approach is scalable and theoretically enables the pooling of entire interaction libraries to interrogate multiple pairwise genetic interactions in a single culture. The speed, ease, and low-cost of our approach makes genetic interaction mapping viable for routine characterization, allowing the interaction network to be used as a universal read out for a variety of biology experiments, and for the elucidation of interaction networks in non-model organisms.
Project description:Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.
Project description:Type 1 diabetes (T1D) is a disease that arises due to complex immunogenetic mechanisms. Key cell-cell interactions involved in the pathogenesis of T1D are activation of autoreactive T cells by dendritic cells (DC), migration of T cells across endothelial cells (EC) lining capillary walls into the islets of Langerhans, interaction of T cells with macrophages in the islets, and killing of β-cells by autoreactive CD8<sup>+</sup> T cells. Overall, pathogenic cell-cell interactions are likely regulated by the individual's collection of genetic T1D-risk variants. To accurately model the role of genetics, it is essential to build systems to interrogate single candidate genes in isolation during the interactions of cells that are essential for disease development. However, obtaining single-donor matched cells relevant to T1D is a challenge. Sourcing these genetic variants from human induced pluripotent stem cells (iPSC) avoids this limitation. Herein, we have differentiated iPSC from one donor into DC, macrophages, EC, and β-cells. Additionally, we also engineered T cell avatars from the same donor to provide an <i>in vitro</i> platform to study genetic influences on these critical cellular interactions. This proof of concept demonstrates the ability to derive an isogenic system from a single donor to study these relevant cell-cell interactions. Our system constitutes an interdisciplinary approach with a controlled environment that provides a proof-of-concept for future studies to determine the role of disease alleles (e.g. <i>IFIH1, PTPN22, SH2B3, TYK2)</i> in regulating cell-cell interactions and cell-specific contributions to the pathogenesis of T1D.
Project description:Cis-regulatory elements such as transcription factor (TF) binding sites can be identified genome-wide, but it remains far more challenging to pinpoint genetic variants affecting TF binding. Here, we introduce a pooling-based approach to mapping quantitative trait loci (QTLs) for molecular-level traits. Applying this to five TFs and a histone modification, we mapped thousands of cis-acting QTLs, with over 25-fold lower cost compared to standard QTL mapping. We found that single genetic variants frequently affect binding of multiple TFs, and CTCF can recruit all five TFs to its binding sites. These QTLs often affect local chromatin and transcription but can also influence long-range chromosomal contacts, demonstrating a role for natural genetic variation in chromosomal architecture. Thousands of these QTLs have been implicated in genome-wide association studies, providing candidate molecular mechanisms for many disease risk loci and suggesting that TF binding variation may underlie a large fraction of human phenotypic variation.
Project description:<h4>Background</h4>High-throughput genetic screening approaches have enabled systematic means to study how interactions among gene mutations contribute to quantitative fitness phenotypes, with the aim of providing insights into the functional wiring diagrams of genetic interaction networks on a global scale. However, it is poorly known how well these quantitative interaction measurements agree across the screening approaches, which hinders their integrated use toward improving the coverage and quality of the genetic interaction maps in yeast and other organisms.<h4>Results</h4>Using large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies.<h4>Conclusions</h4>We have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.
Project description:Recent technological developments in genetic screening approaches have offered the means to start exploring quantitative genotype-phenotype relationships on a large-scale. What remains unclear is the extent to which the quantitative genetic interaction datasets can distinguish the broad spectrum of interaction classes, as compared to existing information on mutation pairs associated with both positive and negative interactions, and whether the scoring of varying degrees of such epistatic effects could be improved by computational means. To address these questions, we introduce here a computational approach for improving the quantitative discrimination power encoded in the genetic interaction screening data. Our matrix approximation model decomposes the original double-mutant fitness matrix into separate components, representing variability across the array and query mutants, which can be utilized for estimating and correcting the single-mutant fitness effects, respectively. When applied to three large-scale quantitative interaction datasets in yeast, we could improve the accuracy of scoring various interaction classes beyond that obtained with the original fitness data, especially in synthetic genetic array (SGA) and in genetic interaction mapping (GIM) datasets. In addition to the known pairs of interactions used in the evaluation of the computational approach, a number of novel interaction pairs were also predicted, along with underlying biological mechanisms, which remained undetected by the original datasets. It was shown that the optimal choice of the scoring function depends heavily on the screening approach and on the interaction class under analysis. Moreover, a simple preprocessing of the fitness matrix could further enhance the discrimination power of the epistatic miniarray profiling (E-MAP) dataset. These systematic evaluation results provide in-depth information on the optimal analysis of the future, large-scale screening experiments. In general, the modeling framework, enabling accurate identification and classification of genetic interactions, provides a solid basis for completing and mining the genetic interaction networks in yeast and other organisms.
Project description:Many biological processes are regulated by RNA-RNA interactions 1, nonetheless it remains formidable to analyze the entire RNA interactome. We developed a method, MARIO (MApping Rna-rna Interactions in vivO), to map protein-assisted RNA-RNA interactions in vivo. By circumventing the selection for a specific RNA-binding protein 2-5, our approach vastly expands the identifiable portion of the RNA interactome. Using this technology, we mapped the RNA interactome in mouse embryonic stem cells, which was composed of 46,780 RNA-RNA interactions. The RNA interactome was a scale-free network, with several lincRNAs and mRNAs emerging as hubs. We validated an interaction between two hubs, Malat1 and Slc2a3 using single molecule RNA fluorescence in situ hybridization. Base pairing was observed at the interaction sites of long RNAs, and was particularly strong in transposonRNA-mRNA and lincRNA-mRNA interactions. This reveals a new type of regulatory sequences acting in trans. Consistent with their hypothesized roles, the RNA interaction sites were more evolutionarily conserved than other regions of the transcripts. MARIO also provided new information on RNA structures, by simultaneously revealing the footprint of single stranded regions and the spatially proximal sites of each RNA. The unbiased mapping of the protein-assisted RNA interactome with minimum perturbation of cell physiology will greatly expand our capacity to investigate RNA functions. Three (3) ESC samples with different treatment (different digestion size and/or crosslinking method) and one (1) MEF sample were included to test our new approach for RNA-interactome mapping and the different samples were analyzed to show RNA interactome differences between them.
Project description:We introduce high-throughput and massive paired-end mapping (PEM), a large-scale genome sequencing method to identify SVs 3 kb or larger that combines the rescue and capture of paired-ends of 3 kb fragments, massive 454 Sequencing, and a computational approach to map DNA reads onto a reference genome. PEM was used to map SVs in an African and putatively European individual and identified shared and divergent SVs relative to the reference genome. Overall, we fine-mapped more than 1000 SVs and documented that the number of SVs among humans is much larger than initially hypothesized; many of the SVs potentially affect gene function. The breakpoint junction sequences of more than 200 SVs were deduced with a novel pooling strategy and computational analysis. Array-CGH was used for validation. Keywords: array CGH 2 samples were analyzed with 8 different Nimblegen chips (385k); thus ~30M probes were used to interrogate copy number variants in NA15510 (using NA18505 as control) at high resolution.
Project description:BACKGROUND: Association mapping studies offer great promise to identify polymorphisms associated with phenotypes and for understanding the genetic basis of quantitative trait variation. To date, almost all association mapping studies based on structured plant populations examined the main effects of genetic factors on the trait but did not deal with interactions between genetic factors and environment. In this paper, we propose a methodological prospect of mixed linear models to analyze genotype by environment interaction effects using association mapping designs. First, we simulated datasets to assess the power of linear mixed models to detect interaction effects. This simulation was based on two association panels composed of 90 inbreds (pearl millet) and 277 inbreds (maize). RESULTS: Based on the simulation approach, we reported the impact of effect size, environmental variation, allele frequency, trait heritability, and sample size on the power to detect the main effects of genetic loci and diverse effect of interactions implying these loci. Interaction effects specified in the model included SNP by environment interaction, ancestry by environment interaction, SNP by ancestry interaction and three way interactions. The method was finally used on real datasets from field experiments conducted on the two considered panels. We showed two types of interactions effects contributing to genotype by environment interactions in maize: SNP by environment interaction and ancestry by environment interaction. This last interaction suggests differential response at the population level in function of the environment. CONCLUSIONS: Our results suggested the suitability of mixed models for the detection of diverse interaction effects. The need of samples larger than that commonly used in current plant association studies is strongly emphasized to ensure rigorous model selection and powerful interaction assessment. The use of ancestry interaction component brought valuable information complementary to other available approaches.
Project description:We describe a combinatorial CRISPR interference (CRISPRi) screening platform for mapping genetic interactions in mammalian cells. We targeted 107 chromatin-regulation factors in human cells with pools of either single or double single guide RNAs (sgRNAs) to downregulate individual genes or gene pairs, respectively. Relative enrichment analysis of individual sgRNAs or sgRNA pairs allowed for quantitative characterization of genetic interactions, and comparison with protein-protein-interaction data revealed a functional map of chromatin regulation.